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Main_3DCNN.py
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Main_3DCNN.py
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# Copyright (c) 2011, Alex Krizhevsky (akrizhevsky@gmail.com)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# - Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# - Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as n
import numpy.random as nr
from util import *
from data import *
from options import *
from gpumodel import *
import sys
import math as m
import layer as lay
from convdata import *
from os import linesep as NL
#import pylab as pl
class ConvNet(IGPUModel):
def __init__(self, op, load_dic, dp_params={}):
filename_options = []
dp_params['multiview_test'] = op.get_value('multiview_test')
dp_params['crop_border'] = op.get_value('crop_border')
IGPUModel.__init__(self, "ConvNet", op, load_dic, filename_options, dp_params=dp_params)
def import_model(self):
lib_name = "pyconvnet" if is_windows_machine() else "_ConvNet"
print "========================="
print "Importing %s C++ module" % lib_name
self.libmodel = __import__(lib_name)
def init_model_lib(self):
self.libmodel.initModel(self.layers, self.minibatch_size, self.device_ids[0])
def init_model_state(self):
ms = self.model_state
if self.load_file:
ms['layers'] = lay.LayerParser.parse_layers(self.layer_def, self.layer_params, self, ms['layers'])
else:
ms['layers'] = lay.LayerParser.parse_layers(self.layer_def, self.layer_params, self)
self.layers_dic = dict(zip([l['name'] for l in ms['layers']], ms['layers']))
logreg_name = self.op.get_value('logreg_name')
if logreg_name:
self.logreg_idx = self.get_layer_idx(logreg_name, check_type='cost.logreg')
# Convert convolutional layers to local
if len(self.op.get_value('conv_to_local')) > 0:
for i, layer in enumerate(ms['layers']):
if layer['type'] == 'conv' and layer['name'] in self.op.get_value('conv_to_local'):
lay.LocalLayerParser.conv_to_local(ms['layers'], i)
# Decouple weight matrices
if len(self.op.get_value('unshare_weights')) > 0:
for name_str in self.op.get_value('unshare_weights'):
if name_str:
name = lay.WeightLayerParser.get_layer_name(name_str)
if name is not None:
name, idx = name[0], name[1]
if name not in self.layers_dic:
raise ModelStateException("Layer '%s' does not exist; unable to unshare" % name)
layer = self.layers_dic[name]
lay.WeightLayerParser.unshare_weights(layer, ms['layers'], matrix_idx=idx)
else:
raise ModelStateException("Invalid layer name '%s'; unable to unshare." % name_str)
self.op.set_value('conv_to_local', [], parse=False)
self.op.set_value('unshare_weights', [], parse=False)
def get_layer_idx(self, layer_name, check_type=None):
try:
layer_idx = [l['name'] for l in self.model_state['layers']].index(layer_name)
if check_type:
layer_type = self.model_state['layers'][layer_idx]['type']
if layer_type != check_type:
raise ModelStateException("Layer with name '%s' has type '%s'; should be '%s'." % (layer_name, layer_type, check_type))
return layer_idx
except ValueError:
raise ModelStateException("Layer with name '%s' not defined." % layer_name)
def fill_excused_options(self):
if self.op.get_value('check_grads'):
self.op.set_value('save_path', '')
self.op.set_value('train_batch_range', '0')
self.op.set_value('test_batch_range', '0')
self.op.set_value('data_path', '')
# Make sure the data provider returned data in proper format
def parse_batch_data(self, batch_data, train=True):
if max(d.dtype != n.single for d in batch_data[2]):
raise DataProviderException("All matrices returned by data provider must consist of single-precision floats.")
return batch_data
def start_batch(self, batch_data, train=True):
data = batch_data[2]
if self.check_grads:
self.libmodel.checkGradients(data)
elif not train and self.multiview_test:
self.libmodel.startMultiviewTest(data, self.train_data_provider.num_views, self.logreg_idx)
else:
self.libmodel.startBatch(data, not train)
def print_iteration(self):
print "%d.%d..." % (self.epoch, self.batchnum),
def print_train_time(self, compute_time_py):
print "(%.3f sec)" % (compute_time_py)
def print_costs(self, cost_outputs):
costs, num_cases = cost_outputs[0], cost_outputs[1]
for errname in costs.keys():
costs[errname] = [(v/num_cases) for v in costs[errname]]
if self.dp_type.find('Kaggle_Galaxy')==-1: # Di Wu add this bespoke condition
print "%s: " % errname,
print ", ".join("%6f" % v for v in costs[errname]),
if sum(m.isnan(v) for v in costs[errname]) > 0 or sum(m.isinf(v) for v in costs[errname]):
print "^ got nan or inf!"
sys.exit(1)
else:
print "Root Mean Sqaured Error: ",
print "%.6f" % (costs[errname][0]/37) ** 0.5,
if sum(m.isnan(v) for v in costs[errname]) > 0 or sum(m.isinf(v) for v in costs[errname]):
print "^ got nan or inf!"
sys.exit(1)
def print_train_results(self):
self.print_costs(self.train_outputs[-1])
def print_test_status(self):
pass
def print_Kaggle_test(self, cost):
print "Root Mean Sqaured Error: ",
print "%.6f" % (cost[0]['sqdiff']/(cost[1]*37)) ** 0.5,
def print_test_results(self):
print ""
print "======================Test output======================"
self.print_costs(self.test_outputs[-1])
print ""
print "-------------------------------------------------------",
for i,l in enumerate(self.layers): # This is kind of hacky but will do for now.
if 'weights' in l:
if type(l['weights']) == n.ndarray:
print "%sLayer '%s' weights: %e [%e]" % (NL, l['name'], n.mean(n.abs(l['weights'])), n.mean(n.abs(l['weightsInc']))),
elif type(l['weights']) == list:
print ""
print NL.join("Layer '%s' weights[%d]: %e [%e]" % (l['name'], i, n.mean(n.abs(w)), n.mean(n.abs(wi))) for i,(w,wi) in enumerate(zip(l['weights'],l['weightsInc']))),
print "%sLayer '%s' biases: %e [%e]" % (NL, l['name'], n.mean(n.abs(l['biases'])), n.mean(n.abs(l['biasesInc']))),
print ""
def conditional_save(self):
self.save_state()
print "-------------------------------------------------------"
print "Saved checkpoint to %s" % os.path.join(self.save_path, self.save_file)
print "=======================================================",
def aggregate_test_outputs(self, test_outputs):
num_cases = sum(t[1] for t in test_outputs)
for i in xrange(1 ,len(test_outputs)):
for k,v in test_outputs[i][0].items():
for j in xrange(len(v)):
test_outputs[0][0][k][j] += test_outputs[i][0][k][j]
return (test_outputs[0][0], num_cases)
@classmethod
def get_options_parser(cls):
op = IGPUModel.get_options_parser()
op.add_option("mini", "minibatch_size", IntegerOptionParser, "Minibatch size", default=128)
op.add_option("layer-def", "layer_def", StringOptionParser, "Layer definition file", set_once=True)
op.add_option("layer-params", "layer_params", StringOptionParser, "Layer parameter file")
op.add_option("check-grads", "check_grads", BooleanOptionParser, "Check gradients and quit?", default=0, excuses=['data_path','save_path','train_batch_range','test_batch_range'])
op.add_option("multiview-test", "multiview_test", BooleanOptionParser, "Cropped DP: test on multiple patches?", default=0, requires=['logreg_name'])
op.add_option("crop-border", "crop_border", IntegerOptionParser, "Cropped DP: crop border size", default=0, set_once=True)
op.add_option("logreg-name", "logreg_name", StringOptionParser, "Cropped DP: logreg layer name (for --multiview-test)", default="")
op.add_option("conv-to-local", "conv_to_local", ListOptionParser(StringOptionParser), "Convert given conv layers to unshared local", default=[])
op.add_option("unshare-weights", "unshare_weights", ListOptionParser(StringOptionParser), "Unshare weight matrices in given layers", default=[])
op.add_option("conserve-mem", "conserve_mem", BooleanOptionParser, "Conserve GPU memory (slower)?", default=0)
op.delete_option('max_test_err')
op.options["max_filesize_mb"].default = 0
op.options["testing_freq"].default = 50
op.options["num_epochs"].default = 50000
op.options['dp_type'].default = None
DataProvider.register_data_provider('MNIST_ConvNetDataProvider', 'MNIST_ConvNetDataProvider', MNIST_ConvNetDataProvider)
DataProvider.register_data_provider('AVLETTERS_ConvNetDataProvider', 'AVLETTERS_ConvNetDataProvider', AVLETTERS_ConvNetDataProvider)
DataProvider.register_data_provider('CodaLab_MemoryConvNetDataProvider', 'CodaLab_MemoryConvNetDataProvider', CodaLab_MemoryConvNetDataProvider)
DataProvider.register_data_provider('CodaLab_ConvNetDataProvider', 'CodaLab_ConvNetDataProvider', CodaLab_ConvNetDataProvider)
DataProvider.register_data_provider('CroppedCodaLab_ConvNetDataProvider', 'CroppedCodaLab_ConvNetDataProvider', CroppedCodaLab_ConvNetDataProvider)
return op
if __name__ == "__main__":
op = ConvNet.get_options_parser()
op, load_dic = IGPUModel.parse_options(op)
model = ConvNet(op, load_dic)
model.start()